*ADRIS MISRA*

Github repository: https://github.com/GreatLearningAIML1/gl-pgp-aiml-uta-intl-aug20-adris-misra

Project - Bank Churn Prediction

Objective:

Given a Bank customer, build a neural network-based classifier that can determine whether they will leave or not in the next 6 months.

Domain:

Learning Outcomes:

Attribute Information:

Deliverable

  1. Read the dataset
  2. Drop the columns which are unique for all users like IDs (5 points)
  3. Perform bivariate analysis and give your insights from the same (5 points)
  4. Distinguish the feature and target set and divide the data set into training and test sets (5 points)
  5. Normalize the train and test data (10points)
  6. Initialize & build the model. Identify the points of improvement and implement the same. (20)
  7. Predict the results using 0.5 as a threshold (10points)
  8. Print the Accuracy score and confusion matrix (5 points)

1. Import the datasets and libraries, check datatype, statistical summary, shape, null values or incorrect imputation

Preliminary data analysis:

  • There are 10000 rows and 14 columns in the dataset
  • There is no null or missing value in any of the columns
  • There are two categorical fields that needs category conversion
  • RowNumber and CustomerID are kind of identity columns so doesn't make sense for any meaningful, also surname is tied to each customer so doesn't bring much insight for prediction
  • All data are not in the same scale, so scaling is required </font>